Towards a computer tool for automatic detection of laryngeal cancer

Ezzine, Kadria; Hamida, Ahmed Ben; Messaoud, Zaineb Ben; Frikha, Mounir

doi:10.1109/atsip.2016.7523111

Cited by 10 publications

(4 citation statements)

References 8 publications

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“…As dysphonia is often the first symptom in LC, many authors have attempted to use ML to detect LC based on voice recordings (Table 4). While previous studies used extracted acoustic features and perturbation measures to differentiate voices of patients with LC from healthy voices, 27 more recent technology converts the acoustic data into a visual Mel spectrogram (a visual representation of the frequency spectrum of an acoustic signal) and feeds an image to the ML model to make such distinction 28 . In a recent study, Kim et al showed accuracy as high as 85% in detecting pathologic voice changes in LC by using voice samples converted to Mel spectrograms 28 .…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Laryngeal Cancer: From Screening to Prognosis: A State of the Art Review

Bensoussan

Vanstrum

Johns

et al. 2022

Otolaryngol.--head neck surg.

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Objective This state of the art review aims to examine contemporary advances in applications of artificial intelligence (AI) to the screening, detection, management, and prognostication of laryngeal cancer (LC). Data Sources Four bibliographic databases were searched: PubMed, EMBASE, Cochrane, and IEEE. Review Methods A structured review of the current literature (up to January 2022) was performed. Search terms related to topics of AI in LC were identified and queried by 2 independent reviewers. Citations of selected studies and review articles were also evaluated to ensure comprehensiveness. Conclusions AI applications in LC have encompassed a variety of data modalities, including radiomics, genomics, acoustics, clinical data, and videomics, to support screening, diagnosis, therapeutic decision making, and prognosis. However, most studies remain at the proof-of-concept level, as AI algorithms are trained on single-institution databases with limited data sets and a single data modality. Implications for Practice AI algorithms in LC will need to be trained on large multi-institutional data sets and integrate multimodal data for optimal performance and clinical utility from screening to prognosis. Out of the data types reviewed, genomics has the most potential to provide generalizable models thanks to available large multi-institutional open access genomic data sets. Voice acoustic data represent an inexpensive and accurate biomarker, which is easy and noninvasive to capture, offering a unique opportunity for screening and monitoring of LA, especially in low-resource settings.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Laryngeal Cancer: From Screening to Prognosis: A State of the Art Review

Bensoussan

Vanstrum

Johns

et al. 2022

Otolaryngol.--head neck surg.

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“…Several ML methods using vocal recordings perform binary classification to distinguish voices from patients with laryngeal cancer from those with healthy voices or benign voice disorders with accuracy ranging from 85.2% to 98% [81][82][83][84][85][86][87][88]89 ], which is derived from a transformation of the audio signal and provides a compact representation of the spectral properties of a sound. Others algorithms rely on acoustic features (jitter, shimmer, and harmonic features) [81,88] and glottal air-flow parameters [84,86]. Studies in this category use various preprocessing, feature extraction, and classifications methods, including different neural network methods [81-84,86,87,89 & ], support vector machine [81,85], hidden Markov models [88], and Gaussian mixture models [85].…”

Section: Machine Learning Models Utilizing Voice and Speech To Screen...mentioning

confidence: 99%

Machine learning in the evaluation of voice and swallowing in the head and neck cancer patient

Srinivasan,

Liu,

Rameau

2023

Current Opinion in Otolaryngology &Amp; Head &Amp; Neck Surgery

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Purpose of review The purpose of this review is to present recent advances and limitations in machine learning applied to the evaluation of speech, voice, and swallowing in head and neck cancer. Recent findings Novel machine learning models incorporating diverse data modalities with improved discriminatory capabilities have been developed for predicting toxicities following head and neck cancer therapy, including dysphagia, dysphonia, xerostomia, and weight loss as well as guiding treatment planning. Machine learning has been applied to the care of posttreatment voice and swallowing dysfunction by offering objective and standardized assessments and aiding innovative technologies for functional restoration. Voice and speech are also being utilized in machine learning algorithms to screen laryngeal cancer. Summary Machine learning has the potential to help optimize, assess, predict, and rehabilitate voice and swallowing function in head and neck cancer patients as well as aid in cancer screening. However, existing studies are limited by the lack of sufficient external validation and generalizability, insufficient transparency and reproducibility, and no clear superior predictive modeling strategies. Algorithms and applications will need to be trained on large multiinstitutional data sets, incorporate sociodemographic data to reduce bias, and achieve validation through clinical trials for optimal performance and utility.

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“…Within this frame, the present research project was designed to test a novel COVID-19 screening tool based on voice analysis through machine learning (ML). Conventional voice analysis proved useful in detecting distinguishing acoustic features of pathologies impairing all structures and systems responsible for phonation, including lungs [17][18][19], trachea [20], larynx [21][22][23], vocal folds [24,25] and central nervous system [26][27][28][29][30]. Furthermore, encouraging results had been obtained for disorders impairing voice production mechanisms only secondarily, including cardiovascular diseases [31][32][33][34] and diabetes [35].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients

et al. 2024

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Towards a computer tool for automatic detection of laryngeal cancer

Cited by 10 publications

References 8 publications

Artificial Intelligence and Laryngeal Cancer: From Screening to Prognosis: A State of the Art Review

Artificial Intelligence and Laryngeal Cancer: From Screening to Prognosis: A State of the Art Review

Machine learning in the evaluation of voice and swallowing in the head and neck cancer patient

Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients

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